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Found 2 papers, 2 papers with code
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions.
Ranked #1 on
Monocular Depth Estimation
on Make3D
Absolute distance prediction based on deep learning object detection and monocular depth estimation models
Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras.
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